class_docstr: A model API that tags input sentence.
  
  Attributes:
      model: Model.
      preprocessor: Transformer. Preprocessing data for feature extraction.
      tokenizer: Tokenize input sentence. Default tokenizer is `str.split`.
docstr: Predict using the model.
  
  Args:
      text: string, the input text.
  
  Returns:
      tags: list, shape = (num_words,)
      Returns predicted values.
docstr_tok: ["Predict","using","the","model",".","Args",":","text",":","string",",","the","input","text",".","Returns",":","tags",":","list",",","shape","=","(","num_words",",",")","Returns","predicted","values","."]
code: def predict(self, text):
      """Predict using the model.
  
      Args:
          text: string, the input text.
  
      Returns:
          tags: list, shape = (num_words,)
          Returns predicted values.
      """
      pred = self.predict_proba(text)
      tags = self._get_tags(pred)
  
      return tags
  
file_path: anago/anago/tagger.py
imports: import numpy
  from seqeval.metrics.sequence_labeling import get_entities
label_desc: Use image node_id 7 for calling the Tagger obj's underlying member method code with example usage: obj.predict(text) and returns: tags